Sem-Fit: a semantic based expert system to provide recommendations in the tourism domain

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The hotel industry is one of the leading stakeholders in the tourism sector. In order to reduce the trav eler’s cost of seeking accommodations, enforce the return ratio efficiency of guest rooms and enhance total operating performance, evaluating and selecting a suitable hotel location has become one of the most critical issues for the hotel industry. In this scenario, recommender services are increasingly emerg ing which employ intelligent agents and artificial intelligence to ‘‘cut through’’ unlimited information and obtain personalized solutions. Taking this assumption into account, this paper presents Sem Fit, a seman tic hotel recommendation expert system, based on the consumer’s experience about recommendation provided by the system. Sem Fit uses the consumer’s experience point of view in order to apply fuzzy logic techniques to relating customer and hotel characteristics, represented by means of domain ontolo gies and affect grids. After receiving a recommendation, the customer provides a valuation about the rec ommendation generated by the system. Based on these valuations, the rules of the system are updated in order to adjust the new recommendations to past user experiences. To test the validity of Sem Fit, the validation accomplished includes the interaction of the customer with the system and then the results are compared with the expert recommendation for each customer profile. Moreover, the values of preci sion and recall and F1 have been calculated, based on three points of view, to measure the degree of rel evance of the recommendations of the fuzzy system, showing that the system recommendations are on the same level as an expert in the domain.
Semantic technologies, Semantic labeling, Fuzzy logic, Recommender systems, Hotels
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Expert Systems with Applications (Sept. 2011), 38(10), 13310–13319